Updated: 2020-08-11 08:50:33 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
South Dakota 1.19 9456 100
North Dakota 1.15 7741 152
West Virginia 1.15 7893 153
Arkansas 1.14 48895 898
Montana 1.12 5105 137
Illinois 1.11 196253 1833
Idaho 1.10 25711 537
Kentucky 1.10 37344 721
Missouri 1.10 55048 1239
Tennessee 1.10 123752 2420
Virginia 1.10 80718 960
Georgia 1.09 202537 3738
Indiana 1.09 76915 990
Oklahoma 1.09 45485 1043
Kansas 1.08 32012 454
Minnesota 1.08 61624 790
Texas 1.08 524631 9075
Wisconsin 1.08 62147 981
Nebraska 1.07 28738 304
Oregon 1.07 21761 355
Alabama 1.06 104482 1805
Mississippi 1.06 69686 1279
Nevada 1.06 58366 1097
Iowa 1.05 49487 507
Ohio 1.05 103042 1329
Washington 1.05 66708 849
Maryland 1.04 97368 914
North Carolina 1.04 139117 1822
California 1.03 579576 8466
Louisiana 1.03 134415 1902
Massachusetts 1.03 121077 394
Michigan 1.03 97607 757
Colorado 1.02 51663 522
Florida 1.02 550652 8715
New Hampshire 1.02 6863 30
Pennsylvania 1.02 124803 872
South Carolina 1.02 103169 1488
Wyoming 1.02 3111 45
New Jersey 1.01 186394 379
New Mexico 1.01 22854 255
Rhode Island 1.01 18009 87
Utah 1.01 44955 479
Delaware 1.00 15472 92
New York 1.00 426318 666
Vermont 1.00 1452 5
Maine 0.99 4072 18
Arizona 0.94 191727 1843
Connecticut 0.94 50433 110

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
King WA 1 1 1.0 16867 780 173
Pierce WA 3 2 1.1 6408 750 116
Spokane WA 5 3 1.1 4514 910 89
Grant WA 9 4 1.2 1580 1670 33
Snohomish WA 4 5 1.0 6288 800 62
Chelan WA 10 6 1.1 1378 1820 34
Clark WA 8 7 1.1 2101 450 31
Yakima WA 2 11 0.9 11039 4430 60
Benton WA 6 12 0.9 3988 2050 40
Franklin WA 7 13 1.0 3702 4080 33
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.0 5009 630 74
Yamhill OR 10 2 1.3 474 460 16
Washington OR 2 3 1.0 3154 540 45
Umatilla OR 4 4 1.0 2377 3090 46
Marion OR 3 5 1.1 2962 880 40
Jackson OR 9 6 1.2 491 230 16
Malheur OR 6 7 1.1 798 2620 16
Clackamas OR 5 8 1.1 1566 390 21
Lane OR 8 10 1.1 598 160 10
Deschutes OR 7 11 1.0 629 350 13
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 1.0 212511 2100 2625
Kern CA 6 2 1.0 25698 2910 738
Fresno CA 7 3 1.1 18211 1860 391
Riverside CA 2 4 1.0 42181 1770 513
San Diego CA 5 5 1.0 33354 1010 438
Santa Clara CA 10 6 1.1 12427 650 259
San Bernardino CA 4 7 1.0 37106 1740 537
Orange CA 3 9 1.0 40633 1280 416
Alameda CA 8 10 1.1 13173 800 203
San Joaquin CA 9 19 0.9 13104 1790 174

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.9 129523 3040 1279
Pima AZ 2 2 1.0 18192 1780 218
Yuma AZ 3 3 0.9 11801 5680 94
Yavapai AZ 10 4 1.0 2068 920 33
Pinal AZ 4 5 0.9 8633 2060 65
Cochise AZ 11 6 1.1 1688 1340 21
Mohave AZ 6 7 0.9 3291 1600 34
Apache AZ 7 8 1.0 3214 4490 21
Coconino AZ 8 10 0.9 3120 2230 17
Navajo AZ 5 12 0.9 5435 5000 21
Santa Cruz AZ 9 13 0.9 2697 5790 11
CO
county ST case rank severity R_e cases cases/100k daily cases
El Paso CO 4 1 1.0 5283 770 78
Adams CO 3 2 1.0 6592 1330 71
Denver CO 1 3 1.0 10390 1500 84
Arapahoe CO 2 4 1.0 7436 1170 61
Jefferson CO 5 5 1.0 4264 750 43
Larimer CO 9 6 1.1 1582 470 24
Boulder CO 7 7 1.1 2104 660 22
Weld CO 6 8 1.0 3746 1270 23
Douglas CO 8 11 1.0 1780 540 18
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 20988 1870 191
Utah UT 2 2 1.0 8910 1510 124
Davis UT 3 3 1.0 3311 970 40
Weber UT 4 4 1.0 2855 1150 34
Washington UT 5 5 1.0 2556 1590 28
Cache UT 6 6 1.1 1936 1580 14
Box Elder UT 12 7 1.1 375 710 6
Tooele UT 9 9 1.0 598 920 7
San Juan UT 8 12 0.9 663 4340 5
Summit UT 7 15 0.9 718 1770 3
NM
county ST case rank severity R_e cases cases/100k daily cases
Chaves NM 11 1 1.2 472 720 17
Cibola NM 8 2 1.1 767 2840 28
Doña Ana NM 4 3 1.0 2510 1170 35
Bernalillo NM 1 4 0.9 5299 780 58
Lea NM 7 5 1.1 816 1160 22
Curry NM 10 6 1.1 574 1140 15
Santa Fe NM 9 7 1.0 672 450 12
Sandoval NM 5 10 0.9 1157 820 9
McKinley NM 2 12 0.9 4078 5600 10
San Juan NM 3 13 0.9 3063 2400 8
Otero NM 6 17 0.8 1114 1690 4

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Bergen NJ 1 1 1.0 21070 2270 38
Camden NJ 9 2 1.0 8663 1710 35
Burlington NJ 12 3 1.0 6077 1360 29
Essex NJ 2 4 1.0 19998 2520 30
Monmouth NJ 8 5 1.0 10446 1680 31
Gloucester NJ 16 6 1.1 3274 1130 22
Passaic NJ 5 7 1.0 17816 3530 24
Union NJ 6 8 1.2 16850 3050 12
Middlesex NJ 4 9 1.0 18157 2200 30
Hudson NJ 3 10 1.0 19839 2970 20
Ocean NJ 7 11 0.9 10721 1810 28
PA
county ST case rank severity R_e cases cases/100k daily cases
Philadelphia PA 1 1 1.0 31487 2000 128
Allegheny PA 4 2 1.0 9144 750 115
Union PA 39 3 1.4 236 520 10
Delaware PA 3 4 1.0 9363 1660 71
Lancaster PA 6 5 1.1 5962 1110 46
York PA 13 6 1.1 2567 580 31
Montgomery PA 2 7 1.0 10180 1240 46
Bucks PA 5 13 1.0 7269 1160 38
Berks PA 7 14 1.0 5409 1300 28
Chester PA 8 16 1.0 5208 1010 37
Lehigh PA 9 21 1.0 4986 1380 20
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.1 13566 1640 190
Baltimore city MD 4 2 1.1 12804 2080 173
Prince George’s MD 1 3 1.0 24363 2690 159
Montgomery MD 2 4 1.0 18531 1780 101
Anne Arundel MD 5 5 1.0 7485 1320 75
Howard MD 6 6 1.0 3909 1240 39
Harford MD 9 7 1.0 2023 810 30
Charles MD 8 9 1.1 2051 1300 22
Frederick MD 7 14 0.9 3107 1250 14
VA
county ST case rank severity R_e cases cases/100k daily cases
Russell VA 69 1 1.6 122 450 8
Virginia Beach city VA 4 2 1.1 5257 1170 130
Mecklenburg VA 29 3 1.5 413 1340 13
Prince William VA 2 4 1.1 9512 2080 73
Scott VA 72 5 1.5 97 440 6
Fairfax VA 1 6 1.1 16426 1440 78
Pittsylvania VA 26 7 1.3 476 770 18
Norfolk city VA 7 8 1.0 3879 1580 80
Chesterfield VA 5 10 1.1 4410 1300 50
Henrico VA 6 14 1.0 3934 1210 44
Loudoun VA 3 18 1.0 5297 1380 31
Newport News city VA 9 19 1.0 1910 1060 34
Arlington VA 8 20 1.1 3087 1330 20
WV
county ST case rank severity R_e cases cases/100k daily cases
Logan WV 9 1 1.5 248 730 17
Grant WV 20 2 1.5 132 1130 9
Mercer WV 11 3 1.3 211 350 10
Raleigh WV 7 4 1.3 254 330 10
Kanawha WV 1 5 1.1 972 520 21
Cabell WV 4 6 1.2 407 430 10
Mingo WV 16 7 1.1 182 740 7
Berkeley WV 3 10 1.0 703 620 6
Ohio WV 6 19 0.9 280 660 3
Monongalia WV 2 20 0.7 968 920 6
Wood WV 8 21 1.0 248 290 1
Jefferson WV 5 25 0.9 297 530 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1 7294 1310 54
Sussex DE 2 2 1 5870 2670 25
Kent DE 3 3 1 2308 1320 14

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Mobile AL 2 1 1.1 10530 2540 251
Jefferson AL 1 2 1.1 13751 2080 244
Clarke AL 38 3 1.5 678 2780 24
Calhoun AL 12 4 1.1 2009 1750 68
Montgomery AL 3 5 1.1 6971 3070 91
Jackson AL 28 6 1.2 1048 2010 31
Talladega AL 22 7 1.2 1338 1660 41
Baldwin AL 6 8 1.0 3834 1840 81
Madison AL 4 9 1.0 5732 1600 90
Tuscaloosa AL 5 10 1.0 4427 2150 58
Shelby AL 7 11 1.0 3616 1710 59
Marshall AL 8 12 1.0 3306 3470 43
Lee AL 9 17 1.0 2917 1830 37
MS
county ST case rank severity R_e cases cases/100k daily cases
Harrison MS 3 1 1.1 2596 1280 63
DeSoto MS 2 2 1.1 3781 2150 72
Jackson MS 5 3 1.1 2449 1720 68
Lee MS 10 4 1.2 1508 1780 41
Hinds MS 1 5 1.0 5882 2430 93
George MS 38 6 1.2 684 2880 32
Tunica MS 61 7 1.3 362 3560 15
Forrest MS 8 13 1.1 1859 2460 33
Washington MS 9 14 1.0 1729 3670 34
Jones MS 7 22 1.0 1951 2850 27
Rankin MS 6 23 0.9 2402 1590 38
Madison MS 4 31 0.9 2512 2430 31
LA
county ST case rank severity R_e cases cases/100k daily cases
Lafayette LA 4 1 1.1 7828 3260 158
East Baton Rouge LA 2 2 1.0 12516 2820 194
St. Landry LA 15 3 1.1 2826 3390 76
Jefferson LA 1 4 1.0 15538 3570 142
St. Tammany LA 7 5 1.0 5321 2110 74
Ouachita LA 8 6 1.0 4963 3180 64
Caddo LA 6 7 1.0 6852 2760 80
Calcasieu LA 5 8 0.9 7175 3580 106
Tangipahoa LA 9 11 1.0 3563 2730 55
Orleans LA 3 14 1.0 10852 2790 62

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Franklin FL 61 1 2.6 474 4040 66
Taylor FL 48 2 2.0 1064 4810 109
Baker FL 51 3 1.8 780 2810 59
Miami-Dade FL 1 4 1.0 138453 5100 2216
Gulf FL 55 5 1.7 670 4170 48
Dixie FL 60 6 1.7 489 2970 31
Broward FL 2 7 1.0 65163 3410 1026
Palm Beach FL 3 9 1.0 38144 2640 523
Hillsborough FL 4 10 1.0 33290 2410 407
Orange FL 5 14 0.9 32476 2460 346
Polk FL 9 15 1.0 14692 2200 222
Duval FL 6 16 1.0 23921 2590 274
Pinellas FL 7 22 1.0 18251 1910 193
Lee FL 8 32 0.9 16916 2350 148
GA
county ST case rank severity R_e cases cases/100k daily cases
Cobb GA 4 1 1.1 13896 1870 294
Gwinnett GA 2 2 1.1 20270 2250 351
Fulton GA 1 3 1.1 20876 2040 363
DeKalb GA 3 4 1.1 14171 1910 220
Richmond GA 9 5 1.1 4551 2260 129
Bleckley GA 127 6 1.6 208 1630 10
Cherokee GA 12 7 1.2 3503 1450 84
Chatham GA 6 8 1.1 5928 2070 119
Hall GA 5 9 1.1 6208 3170 93
Clayton GA 7 14 1.0 5140 1840 81
Muscogee GA 8 20 1.0 4825 2450 66

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Cameron TX 7 1 1.3 18524 4390 926
Karnes TX 79 2 1.8 662 4300 56
Bee TX 53 3 1.7 1109 3390 85
Harris TX 1 4 1.1 88478 1920 1663
Fort Bend TX 11 5 1.3 9617 1300 269
Tarrant TX 4 6 1.1 34599 1710 684
Nueces TX 9 7 1.2 15165 4210 344
Dallas TX 2 9 1.0 55794 2160 584
Hidalgo TX 6 11 1.0 20530 2420 354
El Paso TX 8 12 1.0 16613 1980 238
Travis TX 5 15 1.0 23166 1930 230
Bexar TX 3 16 0.8 45065 2340 498
OK
county ST case rank severity R_e cases cases/100k daily cases
Pittsburg OK 29 1 1.6 352 790 25
Tulsa OK 2 2 1.1 10851 1690 240
Oklahoma OK 1 3 1.1 11034 1410 235
Le Flore OK 27 4 1.4 374 750 23
Cleveland OK 3 5 1.1 3182 1150 75
Sequoyah OK 26 6 1.3 381 920 21
Cherokee OK 18 7 1.3 470 970 21
Rogers OK 6 8 1.1 1036 1140 33
Wagoner OK 7 9 1.1 897 1150 24
Canadian OK 4 11 1.1 1278 930 31
Comanche OK 9 29 1.0 847 690 11
Texas OK 5 41 1.1 1056 5000 3
McCurtain OK 8 45 0.9 870 2640 6

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Macomb MI 3 1 1.1 10767 1240 119
Wayne MI 1 2 1.0 28441 1610 152
Oakland MI 2 3 1.0 15628 1250 112
Kent MI 4 4 1.0 7624 1190 57
Menominee MI 43 5 1.4 140 600 6
Saginaw MI 8 6 1.1 2035 1060 23
Genesee MI 5 7 1.0 3704 900 27
Washtenaw MI 6 10 1.0 3087 840 22
Ottawa MI 9 12 1.0 1867 660 17
Jackson MI 7 30 0.8 2463 1550 9
WI
county ST case rank severity R_e cases cases/100k daily cases
Milwaukee WI 1 1 1.0 21516 2250 235
Waukesha WI 3 2 1.1 4438 1110 112
Washington WI 11 3 1.2 1107 820 32
Barron WI 27 4 1.3 352 780 20
Racine WI 5 5 1.1 3582 1830 49
Dane WI 2 6 1.0 4587 870 50
Sheboygan WI 14 7 1.2 787 680 24
Brown WI 4 13 1.0 4298 1650 38
Outagamie WI 9 15 1.1 1287 700 25
Kenosha WI 6 17 1.0 2734 1620 34
Walworth WI 8 26 1.0 1378 1340 22
Rock WI 7 35 0.9 1590 980 12

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.1 19571 1580 239
Ramsey MN 2 2 1.1 7648 1410 104
Dakota MN 3 3 1.1 4495 1070 77
St. Louis MN 18 4 1.3 560 280 20
McLeod MN 35 5 1.5 180 500 6
Anoka MN 4 6 1.1 3738 1080 55
Washington MN 6 7 1.1 2160 850 37
Scott MN 9 8 1.1 1594 1110 32
Olmsted MN 8 9 1.1 1748 1140 17
Stearns MN 5 16 1.0 2912 1860 12
Nobles MN 7 36 1.0 1765 8080 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Minnehaha SD 1 1 1.1 4437 2380 30
Charles Mix SD 12 2 1.8 102 1090 0
Lincoln SD 3 3 1.1 651 1190 13
Meade SD 15 4 1.3 94 340 3
Brown SD 5 5 1.2 442 1140 5
Pennington SD 2 6 1.0 899 820 8
Brookings SD 7 7 1.3 135 390 2
Union SD 6 8 1.1 218 1440 3
Codington SD 8 9 1.2 133 480 2
Clay SD 9 12 1.1 129 930 2
Beadle SD 4 17 0.8 593 3230 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Sioux ND 14 1 2.0 80 1810 4
Burleigh ND 2 2 1.2 1229 1310 37
Ramsey ND 15 3 1.5 76 660 5
Morton ND 4 4 1.3 374 1220 14
Stark ND 6 5 1.3 273 880 11
Ward ND 7 6 1.2 237 340 8
Cass ND 1 7 1.0 3062 1760 18
Grand Forks ND 3 8 1.0 689 980 7
Williams ND 5 10 1.0 283 830 6
Stutsman ND 9 12 1.0 134 640 3
Benson ND 8 13 0.9 147 2130 6

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Fairfield CT 1 1 0.9 18084 1910 39
Hartford CT 3 2 0.9 12861 1440 30
New Haven CT 2 3 1.0 13207 1540 19
Windham CT 8 4 1.1 737 630 5
New London CT 5 5 1.0 1447 540 5
Tolland CT 7 6 1.0 1072 710 6
Middlesex CT 6 7 1.1 1402 860 2
Litchfield CT 4 8 0.9 1622 890 4
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.1 21892 2760 71
Essex MA 3 2 1.1 17864 2290 64
Middlesex MA 1 3 1.0 26478 1660 79
Norfolk MA 5 4 1.0 10686 1530 48
Worcester MA 4 5 1.0 13681 1660 39
Bristol MA 6 6 1.0 9396 1680 34
Hampden MA 8 7 1.0 7630 1630 23
Plymouth MA 7 8 1.0 9262 1810 19
Barnstable MA 9 10 0.9 1816 850 8
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1 15170 2390 71
Kent RI 2 2 1 1510 920 10
Washington RI 3 3 1 611 480 2
Newport RI 4 4 1 399 480 2
Bristol RI 5 5 1 320 650 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1 232832 2760 299
Suffolk NY 2 2 1 43864 2950 65
Erie NY 7 3 1 8924 970 44
Nassau NY 3 4 1 43718 3220 50
Monroe NY 8 5 1 4981 670 27
Westchester NY 4 6 1 36239 3740 31
Dutchess NY 9 7 1 4622 1570 13
Orange NY 6 10 1 11188 2960 11
Rockland NY 5 12 1 13946 4310 8

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Rutland VT 4 1 1.1 100 170 1
Chittenden VT 1 2 0.9 732 450 1
Bennington VT 5 3 1.0 87 240 0
Windham VT 3 4 1.2 102 240 0
Franklin VT 2 5 0.9 119 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Cumberland ME 1 1 0.9 2096 720 6
York ME 2 2 1.0 680 330 4
Androscoggin ME 3 3 1.0 564 520 2
Penobscot ME 5 4 1.0 154 100 1
Kennebec ME 4 5 0.9 173 140 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.0 3873 940 14
Rockingham NH 2 2 1.1 1695 560 8
Strafford NH 4 3 1.2 356 280 3
Cheshire NH 7 4 1.1 99 130 1
Belknap NH 5 5 1.0 116 190 1
Carroll NH 8 6 1.0 96 200 1
Merrimack NH 3 7 0.9 466 310 1
Grafton NH 6 8 0.6 104 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 9021 2210 137
Florence SC 10 2 1.1 3580 2580 81
Beaufort SC 7 3 1.1 4272 2340 90
Greenville SC 2 4 1.0 11132 2230 114
Aiken SC 15 5 1.1 1954 1170 51
Charleston SC 1 6 0.9 12558 3180 114
Darlington SC 21 7 1.2 1346 2000 37
York SC 9 8 1.0 3660 1420 60
Horry SC 4 10 1.0 8736 2720 77
Spartanburg SC 8 12 1.0 4157 1380 49
Lexington SC 5 13 0.9 5106 1780 59
Berkeley SC 6 17 0.9 4280 2050 50
NC
county ST case rank severity R_e cases cases/100k daily cases
Alleghany NC 80 1 1.6 194 1770 15
Mecklenburg NC 1 2 1.0 22654 2150 224
Wake NC 2 3 1.0 12340 1180 154
Haywood NC 60 4 1.3 450 740 20
Cumberland NC 8 5 1.1 3182 960 62
Guilford NC 4 6 1.0 5749 1100 71
Pitt NC 18 7 1.1 2079 1170 46
Union NC 9 8 1.1 3172 1400 48
Johnston NC 7 9 1.0 3389 1770 48
Forsyth NC 5 10 1.0 5327 1430 52
Gaston NC 6 12 1.0 3413 1580 50
Durham NC 3 15 1.0 6258 2040 50

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Yellowstone MT 1 1 1.1 1328 840 33
Flathead MT 4 2 1.2 361 370 15
Big Horn MT 3 3 1.2 465 3480 20
Silver Bow MT 9 4 1.4 96 280 5
Missoula MT 5 5 1.2 343 300 11
Gallatin MT 2 6 0.9 990 950 14
Lewis and Clark MT 8 7 1.1 168 250 5
Cascade MT 7 8 1.0 182 220 5
Lake MT 6 12 0.8 191 640 3
WY
county ST case rank severity R_e cases cases/100k daily cases
Sheridan WY 12 1 1.2 76 250 3
Carbon WY 9 2 1.1 107 690 4
Laramie WY 1 3 1.0 514 530 6
Teton WY 3 4 0.9 401 1740 8
Fremont WY 2 5 1.0 511 1280 4
Uinta WY 4 6 1.0 282 1370 3
Park WY 7 7 1.1 137 470 3
Natrona WY 6 8 1.0 237 290 3
Sweetwater WY 5 9 0.8 268 610 3
Campbell WY 8 11 0.9 125 260 1
ID
county ST case rank severity R_e cases cases/100k daily cases
Bonneville ID 5 1 1.4 1139 1010 59
Canyon ID 2 2 1.1 6013 2830 139
Ada ID 1 3 1.0 9259 2080 147
Jefferson ID 15 4 1.3 217 780 12
Twin Falls ID 4 5 1.1 1430 1710 27
Kootenai ID 3 6 1.0 1879 1220 37
Fremont ID 24 7 1.3 89 690 5
Jerome ID 9 13 1.1 485 2070 8
Cassia ID 7 14 1.0 539 2280 8
Minidoka ID 8 17 1.0 496 2410 8
Blaine ID 6 25 1.0 578 2630 1
## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Franklin OH 1 1 1.0 18700 1470 201
Cuyahoga OH 2 2 1.0 13752 1100 139
Lucas OH 4 3 1.0 5554 1280 103
Champaign OH 58 4 1.4 184 470 10
Hamilton OH 3 5 1.0 9759 1200 82
Montgomery OH 5 6 1.0 4481 840 65
Summit OH 6 7 1.1 3628 670 50
Butler OH 7 10 1.0 2987 790 40
Mahoning OH 9 23 1.0 2576 1110 22
Marion OH 8 51 1.0 2935 4490 8
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.1 111860 2140 669
LaSalle IL 18 2 1.4 770 700 34
Tazewell IL 23 3 1.4 547 410 26
Bureau IL 41 4 1.5 222 670 14
Jefferson IL 35 5 1.5 276 720 12
DuPage IL 3 6 1.1 12323 1320 107
Peoria IL 13 7 1.2 1676 910 53
Will IL 5 8 1.1 9306 1350 88
Madison IL 9 9 1.1 2665 1000 66
Lake IL 2 11 1.1 12769 1810 96
Kane IL 4 12 1.1 9869 1860 78
St. Clair IL 6 14 1.1 4510 1710 76
McHenry IL 8 19 1.0 3257 1060 37
Winnebago IL 7 36 0.9 3822 1340 20
IN
county ST case rank severity R_e cases cases/100k daily cases
Marion IN 1 1 1.1 16146 1710 170
Vigo IN 30 2 1.4 656 610 27
Sullivan IN 73 3 1.6 119 570 6
St. Joseph IN 5 4 1.1 3552 1320 57
Lake IN 2 5 1.1 7673 1580 71
Allen IN 4 6 1.1 3941 1070 42
Hamilton IN 6 7 1.1 2842 900 45
Vanderburgh IN 7 9 1.1 2045 1130 45
Elkhart IN 3 13 1.0 4982 2450 39
Hendricks IN 8 17 1.1 1917 1190 19
Johnson IN 9 23 1.0 1803 1190 16

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Johnson TN 60 1 1.8 318 1790 31
Weakley TN 50 2 1.6 480 1430 36
Shelby TN 1 3 1.0 24226 2590 368
Benton TN 76 4 1.6 172 1070 14
Knox TN 5 5 1.1 5156 1130 148
Hawkins TN 46 6 1.4 513 910 29
Henry TN 59 7 1.4 324 1000 21
Davidson TN 2 8 1.0 23429 3430 236
Hamilton TN 4 17 1.0 6304 1760 89
Rutherford TN 3 18 1.0 6747 2200 94
Montgomery TN 9 29 1.0 2018 1030 43
Williamson TN 6 34 1.0 3640 1660 50
Wilson TN 8 37 1.0 2346 1770 35
Sumner TN 7 40 1.0 3513 1960 43
KY
county ST case rank severity R_e cases cases/100k daily cases
Jefferson KY 1 1 1.1 8450 1100 181
Fayette KY 2 2 1.2 3991 1250 92
Madison KY 14 3 1.3 510 570 17
Warren KY 3 4 1.0 2686 2120 34
Calloway KY 31 5 1.3 242 620 8
Fulton KY 68 6 1.4 86 1380 4
Hardin KY 11 7 1.1 641 590 15
Kenton KY 4 12 1.0 1454 880 19
Boone KY 5 17 1.0 1121 870 14
Shelby KY 7 18 1.1 772 1650 8
Christian KY 9 20 1.0 660 910 12
Daviess KY 6 24 1.0 782 780 10
Oldham KY 8 32 0.9 669 1020 13

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
St. Louis MO 1 1 1.1 15530 1560 325
Taney MO 15 2 1.4 647 1180 36
Jackson MO 4 3 1.1 4230 610 114
St. Louis city MO 2 4 1.1 5352 1720 101
Jefferson MO 5 5 1.2 1882 840 56
Greene MO 6 6 1.2 1619 560 46
St. Charles MO 3 7 1.0 4348 1110 90
Boone MO 7 14 1.1 1445 820 28
Jasper MO 8 48 0.9 1292 1080 12
Buchanan MO 9 53 0.9 1093 1230 5
AR
county ST case rank severity R_e cases cases/100k daily cases
Jackson AR 57 1 2.2 104 600 8
Logan AR 33 2 1.6 272 1250 16
Chicot AR 19 3 1.4 810 7480 44
Independence AR 22 4 1.4 591 1590 37
Poinsett AR 34 5 1.6 272 1130 16
Mississippi AR 16 6 1.3 1067 2490 44
Sebastian AR 4 7 1.2 2276 1790 68
Pulaski AR 2 8 1.1 5680 1440 101
Craighead AR 7 10 1.1 1402 1330 37
Jefferson AR 5 14 1.1 1574 2240 30
Washington AR 1 16 0.9 6400 2800 48
Crittenden AR 8 17 1.1 1391 2840 23
Benton AR 3 18 1.0 4849 1870 41
Pope AR 9 29 1.0 1364 2140 21
Hot Spring AR 6 32 1.2 1530 4560 6

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1914.4 seconds to compute.
2020-08-11 09:22:27

version history

Today is 2020-08-11.
83 days ago: Multiple states.
75 days ago: \(R_e\) computation.
72 days ago: created color coding for \(R_e\) plots.
67 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
67 days ago: “persistence” time evolution.
60 days ago: “In control” mapping.
60 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
52 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
47 days ago: Added Per Capita US Map.
45 days ago: Deprecated national map.
41 days ago: added state “Hot 10” analysis.
36 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
34 days ago: added per capita disease and mortaility to state-level analysis.
22 days ago: changed to county boundaries on national map for per capita disease.
17 days ago: corrected factor of two error in death trend data.
13 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
8 days ago: added county level “baseline control” and \(R-e\) maps.
4 days ago: fixed normalization error on total disease stats plot.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.